Various sensors including optical sensors have been used to achieve laborsaving and efficient factory automation. Image processors have been used to manage manufacturing processes by capturing an image of a product or a semimanufactured product using an optical sensor. Examples of such an optical sensor include CCD (charge coupled device) or CMOS (complementary Metal Oxide Semiconductor) sensors which are capable of capturing two-dimensional images of a target object of interest.
Some image processors preprocess the captured image by performing a correction process on the original image. Examples of such a correction process include filtering processes for eliminating noises and contrast processes for adjusting an average brightness of the image. Dilating processes and eroding processes on captured images have been known as image filtering processes. An example of the dilating and eroding process is described at page 85 of “Image Processing Standard Textbook—Image Processing” published on Feb. 25, 1997 by Computer Graphic Arts Society, Japan.
Over the past few years, pattern searches and edge detection using color images are becoming prevalent. In conventional systems, the dilating/eroding process has been applied to each of three image planes corresponding to three primary colors (e.g., red, green, and blue colors). Such systems have not been able to eliminate noises well, but rather have been susceptible to generation of “false color” noises.
In view of the above, it would be desirable to have improved systems which are capable of suppressing noises for color images.